我国汽车产业线上供应链金融信用风险评价研究

发布时间：2018-01-21 09:15

本文关键词： 线上供应链金融汽车产业信用风险 BP神经网络　出处：《首都经济贸易大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着信息化的发展,供应链金融逐步开启了其“线上”化的革命,它是在信息化、电子化、平台化协同发展的基础之上,在实体产业供应链和金融行业之间产生的中小企业融资业务新趋势。而在当前众多开展线上供应链金融业务的行业中,汽车产业发展的最为迅速。一方面汽车产业是我国工业中的重点产业,占国民生产总值的较大比重;另一方面,汽车产业链较为成熟完善,随着汽车产业的快速发展,汽车产业链上下游以零部件供应商和汽车经销商为代表的中小企业融资难的问题也日益明显。不同于传统的银行融资和供应链金融业务,由于参与线上供应链金融业务的主体更多,相应的就会导致其风险因素更为复杂,从而加大了商业银行面临的信用风险程度及评价难度。本文以此为研究背景,以汽车产业为代表,对线上供应链金融信用风险进行系统性的研究,并构建评风险价模型进行实证研究。本文以线上供应链金融和汽车产业线上供应链金融的相关理论和实践为切入点,系统的研究了汽车产业线上供应链金融信用风险概念和特征、信用风险产生原因以及信用风险评价方法特点,通过对比当前较为常见的几种信用风险评价方法,对其优缺点和适用性进行分析,最终选定BP神经网络为本文的评价方法。在大量阅读文献和产业调研、问卷调查相结合的基础上,初选了50个风险评价指标,并通过相关性分析、专家打分法从定量、定性两方面对上述初选指标进行了筛选,经过分析取舍,最终构建了包括25个指标在内的风险评价体系。在BP神经网络风险评价模型构建与实现部分,首先对BP神经网络的原理、算法及步骤进行了解释,通过向北京某商业银行的相关工作人员发放调查问卷,最终得到十二份有效数据。通过对BP神经网络模型的结构、函数、相关参数的设定最终完成信用风险评价模型的构建。将收集到的数据进行归一化处理,随机选取三组数据作为验证样本,其余作为训练样本,用构建好模型进行仿真来验证该模型的有效性,仿真结果与验证样本数据基本一致,证明该模型具有较高的准确性和有效性。对商业银行和相关中小企业在开展线上供应链金融业务时的对企业信用风险评价提供良好的借鉴作用。
[Abstract]:With the development of informatization, supply chain finance has gradually opened its "on-line" revolution, which is based on the collaborative development of informatization, electronization and platformization. Between the real industry supply chain and the financial industry, there is a new trend of financing business of small and medium-sized enterprises. On the one hand, the automobile industry is the key industry in our country, which accounts for a large proportion of the gross national product (GNP). On the other hand, automobile industry chain is more mature and perfect, with the rapid development of automobile industry. The problem of financing difficulty of small and medium-sized enterprises represented by parts suppliers and automobile dealers in the upstream and downstream of automobile industry chain is becoming more and more obvious, which is different from the traditional banking and supply chain financial business. As more participants in online supply chain financial business, the corresponding risk factors will be more complex, thus increasing the degree of credit risk faced by commercial banks and evaluation difficulties. This paper takes this as the research background. Take the automobile industry as the representative, carries on the systematic research to the on-line supply chain finance credit risk. This paper starts with the theory and practice of online supply chain finance and automotive industry online supply chain finance. This paper systematically studies the concept and characteristics of credit risk of supply chain finance in automobile industry line, the causes of credit risk and the characteristics of credit risk evaluation method, and compares several common credit risk evaluation methods. On the basis of analyzing its advantages and disadvantages and applicability, BP neural network is selected as the evaluation method of this paper. 50 primary risk evaluation indicators were selected, and through correlation analysis, the expert scoring method from the quantitative and qualitative aspects of the selection of the above primary indicators, after analysis and selection. Finally, the risk evaluation system including 25 indexes is constructed. In the part of constructing and implementing the risk evaluation model of BP neural network, the principle, algorithm and steps of BP neural network are explained. Through the distribution of questionnaires to the relevant staff of a commercial bank in Beijing, 12 valid data were obtained, and the structure and function of BP neural network model were analyzed. Finally, the establishment of the relevant parameters completes the construction of the credit risk assessment model. The collected data is normalized, and three groups of data are randomly selected as validation samples and the rest as training samples. The validity of the model is verified by building a good model, and the simulation results are basically consistent with the validation sample data. It is proved that the model is accurate and effective, which can be used as a good reference for the credit risk evaluation of commercial banks and small and medium-sized enterprises in the online supply chain financial business.
【学位授予单位】：首都经济贸易大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F426.471;F832.4;F274

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